{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import sys\nsys.path.append(\"../input/pytorch-version\")\nfrom shutil import copyfile\n\n#copy our file into the working directory (make sure it has .py suffix)\n#copyfile(src = \"../input/pytorch-version/pytorch_version\", dst = \"../working/pytorch-version\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom PIL import Image, ImageFile\nfrom torch.utils.data import Dataset\nimport torch\nfrom torchvision import transforms\nimport os\nfrom pytorch_version.model import DRModel\ndevice = torch.device(\"cuda:0\")\nImageFile.LOAD_TRUNCATED_IMAGES = True\nmodel = DRModel(device)\ncheckpt = torch.load('../input/pytorch-version/pytorch_version/models/resnet101_mse_dim_64/resnet101_mse_dim_64_fold_4.pth')\ntransform = transforms.Compose([\n    transforms.Resize(256),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                         std=[0.229, 0.224, 0.225])\n    ])\nmodel.load_state_dict(checkpt['model_state_dict'])\nfor param in model.parameters():\n    param.requires_grad = False\n\nmodel.eval()\n\n\nclass RetinopathyDatasetTest(Dataset):\n    def __init__(self, csv_file, dim, transform):\n        self.data = pd.read_csv(csv_file)\n        self.transform = transform\n        self.dim = dim\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, idx):\n        img_name = os.path.join('../input/aptos2019-blindness-detection/test_images', self.data.loc[idx, 'id_code'] + '.png')\n        image = Image.open(img_name)\n        image = image.resize((self.dim, self.dim), resample=Image.BILINEAR)\n        image = self.transform(image)\n        return {'image': image}\n\ntest_dataset = RetinopathyDatasetTest('../input/aptos2019-blindness-detection/sample_submission.csv', 256, transform)\ntest_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=0)\ntest_preds = np.zeros((len(test_dataset), 1))\ntk0 = tqdm(test_data_loader)\nfor i, x_batch in enumerate(tk0):\n    x_batch = x_batch[\"image\"]\n    pred = model(x_batch.to(device))\n    test_preds[i * 32:(i + 1) * 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1)\n\ncoef = [0.5, 1.5, 2.5, 3.5]\n\nfor i, pred in enumerate(test_preds):\n    if pred < coef[0]:\n        test_preds[i] = 0\n    elif coef[0] <= pred < coef[1]:\n        test_preds[i] = 1\n    elif coef[1] <= pred < coef[2]:\n        test_preds[i] = 2\n    elif coef[2] <= pred < coef[3]:\n        test_preds[i] = 3\n    else:\n        test_preds[i] = 4\n\n\nsample = pd.read_csv(\"../input/aptos2019-blindness-detection/sample_submission.csv\")\nsample.diagnosis = test_preds.astype(int)\nsample.to_csv(\"submission1.csv\", index=False)\n","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}